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The integration of the key events of the AOP for predicting skin sensitizers showed improved prediction performance over single models [27 30].
Our most important finding is that incorporating higher-order representative features leads to improved prediction performance, which may help build better predictive models for cancer prognosis.
Several factors can contribute to the improved prediction performance of our approach in predicting RD values from sequences alone.
The HAc ChIP-Seq signal also improved prediction performance (by 14%), but the improvement was highly variable from TF to TF (coefficient of variation = 27%; see Supplementary Table S5).
The integration of multiple assays as key events in the AOP has been shown to have improved prediction performance.
Consequently, multiple studies have used D instead of A to achieve improved prediction performance [3, 12 14].
Similar(29)
Dimensionality reduction techniques are often performed prior to applying the Cox model to improve prediction performance.
This is an important fact to stress since RactIP is expected to improve prediction performance in unknown target search in long genomes by predicting respective intramolecular structures as well as intermolecular binding sites in practical time.
Therefore, the introduction of this method will improve prediction performance.
Previous studies (Tasnim et al. 2014) indicate the ensemble learning can improve prediction performance.
This improves prediction performance since some predictors only use causal pixels for data prediction, such as MED predictor.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com